AMD Ryzen Z2 Go GPU vs NVIDIA RTX 4000 Mobile Ada Generation Comparison

Head-to-Head Benchmarks

The recorded database contains no head-to-head benchmark entries for this pairing, and both products hold identical percentile rankings at 50.0. This absence of direct measurement data means the comparison must rely on the architectural specifications and theoretical throughput values recorded for each part. The AMD Ryzen Z2 Go GPU delivers 4.147 TFLOPS of FP32 compute, while the NVIDIA RTX 4000 Mobile Ada Generation reaches 24.72 TFLOPS. This difference represents a 5.96x advantage for the NVIDIA part in raw single-precision floating-point throughput, a gap that would be decisive in any compute-heavy workload where FP32 performance scales linearly.

In texture processing, the NVIDIA part records 386.3 GTexel/s against the AMD part's 129.6 GTexel/s, a 2.98x margin. Pixel throughput shows the NVIDIA part at 133.2 GPixel/s versus 86.40 GPixel/s for the AMD part, a 1.54x advantage. Memory bandwidth favors the NVIDIA part substantially, with 432.0 GB/s compared to 102.4 GB/s, a 4.22x difference. These figures indicate that the NVIDIA part would dominate in any fill-rate-bound or bandwidth-bound scenario, such as high-resolution rasterization with heavy texture sampling or large framebuffer effects.

The FP16 comparison reveals a notable architectural difference. The AMD part delivers 8.294 TFLOPS of FP16, achieved through a 2:1 ratio relative to FP32. The NVIDIA part delivers 24.72 TFLOPS of FP16 at a 1:1 ratio, meaning it does not double throughput for half-precision operations. The NVIDIA part still leads in absolute FP16 throughput by 2.98x, but the AMD part's ability to use FP16 for twice its FP32 rate could narrow the gap in workloads explicitly optimized for half-precision math. The absence of benchmark scores means these theoretical figures cannot be validated against real-world application performance, but they establish the relative compute ceilings for each product.

The RTX 4000 Mobile Ada Generation also carries dedicated tensor cores, with 232 units recorded, while the AMD part lists no tensor core count. This indicates the NVIDIA part supports hardware-accelerated matrix operations, which would be necessary for DLSS-style upscaling, AI inference, and certain scientific computing tasks. The AMD part's 12 ray tracing cores compare to the NVIDIA part's 58, a 4.83x difference in RT hardware count. Both products support DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4, so API-level feature parity exists despite the internal architectural divergence.

Architecture Differences

The two products come from different manufacturing processes. The AMD Ryzen Z2 Go GPU uses a 6 nm process at TSMC, while the NVIDIA RTX 4000 Mobile Ada Generation uses a 5 nm process, also at TSMC. The smaller process node contributes to the NVIDIA part's higher transistor density, recorded at 121.8M transistors per square millimeter versus 63.0M for the AMD part. The NVIDIA chip, designated AD104, contains 35,800 million transistors on a 294 mm² die. The AMD chip, designated Rembrandt+, contains 13,100 million transistors on a 208 mm² die. This means the NVIDIA part fits 2.73x more transistors into a 1.41x larger die area.

The AMD part uses RDNA 2.0 architecture, while the NVIDIA part uses Ada Lovelace. These are fundamentally different GPU designs with different scheduling, cache hierarchies, and execution resource allocations. The AMD part has 768 shading units, 48 texture mapping units, and 32 render output units. The NVIDIA part has 7,424 shading units, 232 TMUs, and 80 ROPs. The NVIDIA part therefore has 9.67x more shading units, 4.83x more TMUs, and 2.5x more ROPs. This resource distribution suggests the NVIDIA part is designed for much higher instruction-level parallelism and wider memory transactions.

Clock behavior differs significantly. The AMD part runs at a base clock of 800 MHz and a boost clock of 2700 MHz. The NVIDIA part runs at a base clock of 1290 MHz and a boost clock of 1665 MHz. The AMD part's boost clock is 1.62x higher than the NVIDIA part's boost clock, which partially compensates for the NVIDIA part's wider execution resources in certain latency-bound workloads. However, the NVIDIA part's base clock is 1.61x higher than the AMD part's base clock, suggesting the AMD part relies more heavily on boost behavior to reach its performance envelope.

Memory architecture diverges completely. The AMD part uses 16 GB of LPDDR5 on a 128-bit bus, yielding 102.4 GB/s of bandwidth. The NVIDIA part uses 12 GB of GDDR6 on a 192-bit bus, yielding 432.0 GB/s. The NVIDIA part has a 50% wider memory bus and a 4.22x higher bandwidth, but 4 GB less capacity. The AMD part's memory runs at 800 MHz with 6.4 Gbps effective transfer, while the NVIDIA part's memory runs at 2250 MHz with 18 Gbps effective transfer. The memory type difference also implies different power characteristics, with LPDDR5 typically drawing less power than GDDR6, though exact power figures for the memory subsystem are not recorded.

Power consumption is recorded as a 28 W TDP for the AMD part and a 110 W TDP for the NVIDIA part. This 3.93x difference in thermal design power aligns with the compute throughput differences, but the AMD part delivers 0.148 TFLOPS per watt of FP32 performance, while the NVIDIA part delivers 0.225 TFLOPS per watt. The NVIDIA part is therefore more power-efficient in raw FP32 throughput per watt, despite its higher absolute power draw. The AMD part's lower power envelope makes it suitable for compact, passively cooled or lightly cooled designs, while the NVIDIA part requires more substantial thermal management.

The NVIDIA part uses a PCIe 4.0 x16 bus interface, while the AMD part has no recorded bus interface. The NVIDIA part is classified as an IGP (integrated graphics processor) in slot width, suggesting it is designed for mobile integration rather than add-in card use. The AMD part has a display output of 1x USB Type-C, while the NVIDIA part's display outputs are recorded as "Portable Device Dependent." Neither product has power connectors, and both lack length, height, and width dimensions in the database.

Where Each One Wins

The AMD Ryzen Z2 Go GPU wins in scenarios where low power draw is the primary constraint. Its 28 W TDP allows for deployment in thin-and-light devices where thermal budgets are tight, such as handheld gaming consoles or ultraportable laptops. The higher boost clock of 2700 MHz also gives it an advantage in single-threaded or lightly threaded workloads that cannot saturate the NVIDIA part's wider but slower-clocked execution resources. The 16 GB memory capacity exceeds the NVIDIA part's 12 GB, which would be beneficial in applications that need large framebuffer allocations or in-memory dataset residency, even though the AMD part's bandwidth is much lower.

The AMD part also wins in FP16-to-FP32 throughput ratio. Its 2:1 FP16 ratio means that workloads using half-precision math can achieve 8.294 TFLOPS, which is exactly double its FP32 rate. The NVIDIA part's 1:1 ratio means FP16 workloads run at the same 24.72 TFLOPS as FP32. So while the NVIDIA part still has higher absolute FP16 throughput, the AMD part's relative advantage in FP16 efficiency could matter for applications that alternate between precision modes, as the transition from FP32 to FP16 costs nothing on the AMD part but also gains nothing on the NVIDIA part.

The NVIDIA RTX 4000 Mobile Ada Generation wins in every raw throughput category recorded. Its FP32 performance is 5.96x higher, its texture rate is 2.98x higher, its pixel rate is 1.54x higher, and its memory bandwidth is 4.22x higher. These advantages translate directly to higher frame rates in games, faster rendering in content creation applications, and shorter completion times for compute tasks. The 232 tensor cores provide a hardware path for AI-accelerated workloads that the AMD part lacks entirely, as no tensor core count is recorded for the RDNA 2.0 part. The 58 RT cores versus 12 give the NVIDIA part a substantial edge in ray-traced rendering, with 4.83x more RT hardware available.

The NVIDIA part's higher base clock of 1290 MHz versus 800 MHz means it maintains a higher minimum performance floor under sustained load, provided the thermal solution can handle the 110 W TDP. The AMD part's boost clock of 2700 MHz is higher, but boost clocks are typically only sustainable for short bursts before thermal throttling in power-limited designs. The NVIDIA part's memory bandwidth advantage of 432.0 GB/s versus 102.4 GB/s is critical for 4K resolution gaming, where each frame requires significantly more data movement than at 1080p. The AMD part's memory capacity advantage of 16 GB versus 12 GB may help with texture streaming at very high quality settings, but the bandwidth deficit would likely bottleneck that capacity in practice.

The production status for both parts is recorded as "Active," and both have a release date in the database. The AMD part's release date is listed as 2024-12-31, while the NVIDIA part's release date is 2023-03-20. The NVIDIA part has a recorded predecessor (Ampere-MW) and successor (Blackwell-MW), while the AMD part has neither. This suggests the NVIDIA part sits in a more defined product lineage, while the AMD part may be a standalone or first-generation entry in its category.

FAQ

Q: Which part has higher FP32 compute throughput?

A: The NVIDIA RTX 4000 Mobile Ada Generation delivers 24.72 TFLOPS of FP32, while the AMD Ryzen Z2 Go GPU delivers 4.147 TFLOPS. The NVIDIA part is 5.96x faster in this metric.

Q: How do the memory subsystems compare?

A: The AMD part uses 16 GB of LPDDR5 on a 128-bit bus with 102.4 GB/s bandwidth. The NVIDIA part uses 12 GB of GDDR6 on a 192-bit bus with 432.0 GB/s bandwidth. The NVIDIA part has 4.22x more bandwidth but 4 GB less capacity.

Q: Does either part support ray tracing?

A: Both parts support DirectX 12 Ultimate (12_2), which includes ray tracing requirements. The AMD part has 12 RT cores, while the NVIDIA part has 58 RT cores, a 4.83x difference in dedicated ray tracing hardware.

Q: What is the power consumption difference?

A: The AMD part has a TDP of 28 W, while the NVIDIA part has a TDP of 110 W. The NVIDIA part draws 3.93x more power but also delivers 5.96x more FP32 throughput, making it more efficient in TFLOPS per watt.

Q: Are there any tensor core differences?

A: The NVIDIA part records 232 tensor cores, while the AMD part has no tensor core count listed. This indicates the NVIDIA part includes dedicated hardware for matrix operations and AI workloads, which the AMD part lacks.

Q: Which part has a higher boost clock?

A: The AMD part boosts to 2700 MHz, while the NVIDIA part boosts to 1665 MHz. The AMD part's boost clock is 1.62x higher, but its base clock of 800 MHz is 0.62x of the NVIDIA part's 1290 MHz base clock.

Specification Differences

| Specification | AMD Ryzen Z2 Go GPU | NVIDIA RTX 4000 Mobile Ada Generation |

|---|---|---|

| Architecture | RDNA 2.0 | Ada Lovelace |

| Process Node | 6 nm | 5 nm |

| Transistors | 13,100 million | 35,800 million |

| Die Size | 208 mm² | 294 mm² |

| Transistor Density | 63.0M / mm² | 121.8M / mm² |

| Base Clock | 800 MHz | 1290 MHz |

| Boost Clock | 2700 MHz | 1665 MHz |

| Memory Clock | 800 MHz, 6.4 Gbps effective | 2250 MHz, 18 Gbps effective |

| Memory Size | 16 GB | 12 GB |

| Memory Type | LPDDR5 | GDDR6 |

| Memory Bus Width | 128 bit | 192 bit |

| Memory Bandwidth | 102.4 GB/s | 432.0 GB/s |

| Shading Units | 768 | 7,424 |

| TMUs | 48 | 232 |

| ROPs | 32 | 80 |

| RT Cores | 12 | 58 |

| Tensor Cores | None recorded | 232 |

| Pixel Rate | 86.40 GPixel/s | 133.2 GPixel/s |

| Texture Rate | 129.6 GTexel/s | 386.3 GTexel/s |

| FP32 | 4.147 TFLOPS | 24.72 TFLOPS |

| FP16 | 8.294 TFLOPS (2:1) | 24.72 TFLOPS (1:1) |

| TDP | 28 W | 110 W |

| Slot Width | None recorded | IGP |

| Bus Interface | None recorded | PCIe 4.0 x16 |

| Display Outputs | 1x USB Type-C | Portable Device Dependent |

| Release Date | 2024-12-31 | 2023-03-20 |

| Predecessor | None recorded | Ampere-MW |

| Successor | None recorded | Blackwell-MW |

The Verdict

The data shows a clear performance hierarchy between these two parts, but the selection depends entirely on the intended use case. The NVIDIA RTX 4000 Mobile Ada Generation is the superior choice for any workload that demands high absolute throughput: FP32 compute, texture filtering, pixel fill, memory bandwidth, ray tracing, or tensor operations. Its 24.72 TFLOPS of FP32, 386.3 GTexel/s texture rate, 133.2 GPixel/s pixel rate, and 432.0 GB/s bandwidth place it in a different performance class from the AMD part. The 232 tensor cores and 58 RT cores provide hardware acceleration for AI and ray-traced workloads that the AMD part cannot match. The NVIDIA part also shows better power efficiency in FP32 per watt, delivering 0.225 TFLOPS/W versus the AMD part's 0.148 TFLOPS/W, despite its 110 W TDP.

The AMD Ryzen Z2 Go GPU is the appropriate choice for designs where power draw is the dominant constraint. Its 28 W TDP enables deployment in devices with minimal cooling and small batteries, where the NVIDIA part's 110 W requirement would be impractical. The AMD part's 16 GB memory capacity exceeds the NVIDIA part's 12 GB, which could benefit applications that require large framebuffers or in-memory data residency, though the 102.4 GB/s bandwidth would limit how quickly that memory can be accessed. The higher boost clock of 2700 MHz gives the AMD part an advantage in bursty, latency-sensitive workloads that do not require sustained full-load throughput.

The release dates suggest the AMD part is newer, with a listed date of 2024-12-31 versus the NVIDIA part's 2023-03-20, but the NVIDIA part's established product lineage (predecessor Ampere-MW, successor Blackwell-MW) indicates a more mature ecosystem. Both parts are marked as Active in production status. The database records no benchmark scores for either product, so the theoretical specifications here represent the only measurable comparison points. For a user prioritizing raw performance and AI acceleration, the NVIDIA part is the only choice. For a user prioritizing ultra-low power consumption and memory capacity in a compact mobile device, the AMD part offers a viable alternative, but the performance gap of 5.96x in FP32 and 4.22x in memory bandwidth is substantial and would be evident in nearly any demanding workload.